With the advance of sensor technologies and the rapid breakthrough in computing, real-world data analysis has evolved to use voluminous high-dimensional datasets for modeling various systems with great complexity [1]}, [2]}. A major challenge of analyzing a high-dimensional dataset comes from the curse of dimensionality [3]}. That is, in a high-dimensional space, data tend to be sparser than in a low dimensional space so signals are much weaker but noises have far greater impacts producing misleading analysis outcomes.
A fundamental solution for such a challenge is dimensionality reduction or variable selection, i.e., to identify a low-dimensional subset of features that can represent the entire data with a minimal loss of information [3]}.
